1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.

NB: no maps in the interests of speed

4.1 Data

We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 CREDS place-based emissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

  • Emissions are presented as per capita…
  • Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
##        region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: South East     64          10435.31        3333.118

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##           LSOA11NM                      WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 001B     Wonston and Micheldever       1141        1221       693
## 2: Winchester 014C                     Denmead       1020        1112       731
## 3: Winchester 003A                 St Barnabas        896         896       442
## 4: Winchester 004E Alresford and Itchen Valley        844         876       534
## 5: Winchester 013D       Southwick and Wickham        824        1304       901
## 6: Winchester 007A                  St Michael        814        1138       884
##           LSOA11NM                  WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 013D   Southwick and Wickham        824        1304       901
## 2: Winchester 001B Wonston and Micheldever       1141        1221       693
## 3: Winchester 007A              St Michael        814        1138       884
## 4: Winchester 014C                 Denmead       1020        1112       731
## 5: Winchester 014A   Southwick and Wickham        726         977       744
## 6: Winchester 004A       Upper Meon Valley         48         973       505
##           LSOA11NM                      WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 001B     Wonston and Micheldever       1141        1221       693
## 2: Winchester 014C                     Denmead       1020        1112       731
## 3: Winchester 003A                 St Barnabas        896         896       442
## 4: Winchester 004E Alresford and Itchen Valley        844         876       534
## 5: Winchester 013D       Southwick and Wickham        824        1304       901
## 6: Winchester 007A                  St Michael        814        1138       884

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values
Table 4.1: Data summary
Name …[]
Number of rows 64
Number of columns 9
Key NULL
_______________________
Column type frequency:
numeric 9
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDStotal_kgco2e_pdw 0 1 24933.52 8685.63 11275.19 18731.61 24005.72 31004.27 47031.62 ▆▇▆▂▁
CREDSgas_kgco2e2018_pdw 0 1 2453.54 611.06 102.68 2169.00 2454.65 2840.90 3783.79 ▁▁▅▇▂
CREDSelec_kgco2e2018_pdw 0 1 1132.35 254.86 778.32 956.46 1081.99 1238.38 1896.98 ▇▇▂▂▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw 0 1 3585.90 662.13 1999.65 3107.95 3567.58 3977.05 5225.86 ▂▅▇▅▁
CREDSotherEnergy_kgco2e2011_pdw 0 1 243.91 374.48 17.96 52.53 90.85 222.03 2048.92 ▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw 0 1 3829.80 706.14 2416.16 3388.92 3855.76 4315.99 5354.20 ▅▇▇▇▃
CREDScar_kgco2e2018_pdw 0 1 3023.43 930.43 1178.16 2275.68 3082.84 3832.39 4673.38 ▅▇▇▇▇
CREDSvan_kgco2e2018_pdw 0 1 390.30 390.33 55.18 139.57 292.94 522.73 2093.34 ▇▂▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw 0 1 3413.73 1132.67 1252.96 2563.84 3379.91 4292.45 6447.09 ▃▇▆▃▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level all per dwelling emissions against IMD score

Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -6.1744, df = 62, p-value = 5.634e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7492275 -0.4376306
## sample estimates:
##        cor 
## -0.6170604
##    LSOA11CD            WD18NM          All_Tco2e_per_dw
##  Length:64          Length:64          Min.   :11.28   
##  Class :character   Class :character   1st Qu.:18.73   
##  Mode  :character   Mode  :character   Median :24.01   
##                                        Mean   :24.93   
##                                        3rd Qu.:31.00   
##                                        Max.   :47.03
##     LSOA11CD                           WD18NM All_Tco2e_per_dw
## 1: E01023241          Wonston and Micheldever         47.03162
## 2: E01023242 Badger Farm and Oliver's Battery         45.19596
## 3: E01023268                          St Paul         44.31907
## 4: E01032859           Whiteley and Shedfield         38.08636
## 5: E01023270                          St Paul         37.36082
## 6: E01023271           Whiteley and Shedfield         37.18580
##     LSOA11CD                WD18NM All_Tco2e_per_dw
## 1: E01023285 Southwick and Wickham         12.45964
## 2: E01023256        St Bartholomew         11.79560
## 3: E01023252            St Michael         11.77504
## 4: E01023250           St Barnabas         11.64420
## 5: E01023221      Bishop's Waltham         11.30985
## 6: E01023261               St Luke         11.27519

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   102.7  2169.0  2454.7  2453.5  2840.9  3783.8
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level gas per dwelling emissions against IMD score

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -5.42, df = 62, p-value = 1.036e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7133838 -0.3732082
## sample estimates:
##        cor 
## -0.5670019

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.357040  0.127705
## sample estimates:
##        cor 
## -0.1219319

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.357040  0.127705
## sample estimates:
##        cor 
## -0.1219319
##                          RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1:       Rural town and fringe        2454.397         1090.922                183.53342
## 2: Rural village and dispersed        2276.440         1543.172                824.72984
## 3:         Urban city and town        2521.518         1001.396                 59.97038

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 5.8031, df = 62, p-value = 2.389e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4067955 0.7322961
## sample estimates:
##       cor 
## 0.5932803
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 5.8533, df = 62, p-value = 1.968e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4110607 0.7346623
## sample estimates:
##       cor 
## 0.5965891
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level car use per dwelling emissions against IMD score

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -4.9256, df = 62, p-value = 6.568e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6866312 -0.3271725
## sample estimates:
##        cor 
## -0.5303315
##                          RUC11 mean_car_kgco2e mean_van_kgco2e
## 1:       Rural town and fringe        3373.676        396.8821
## 2: Rural village and dispersed        3881.775        805.6082
## 3:         Urban city and town        2453.895        225.0763

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level van use per dwelling emissions against IMD score

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.69702, df = 62, p-value = 0.4884
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1611247  0.3269007
## sample estimates:
##        cor 
## 0.08817693

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   227.0   354.0   407.0   438.6   493.2   901.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   479.0   649.2   727.0   756.1   865.2  1304.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.3 Estimating the annual emissions levy

Case studies:

  • Annual carbon tax
  • Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

EU carbon ‘price’

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

  • based on a Marginal Abatement Cost (MAC)
  • 2021:
    • Low: £122/T
    • Central: £245/T <- use the central value for now
    • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: Central cost

The table below shows the overall £ GBP total for the case study area in £M.

## £m total
##    nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:     64        286.0057         28.69601             13.58922
## £m by regions covered
##        region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: South East     64        286.0057         28.69601             13.58922

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  6110                       600                        280
##    beis_GBPtotal_c_energy_perdw
## 1:                          880

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.7: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2762    4589    5881    6109    7596   11523
##     LSOA11CD        LSOA01NM                           WD18NM CREDStotal_kgco2e_pdw
## 1: E01023241 Winchester 003B          Wonston and Micheldever              47031.62
## 2: E01023242 Winchester 009B Badger Farm and Oliver's Battery              45195.96
## 3: E01023268 Winchester 005D                          St Paul              44319.07
## 4: E01032859 Winchester 013F           Whiteley and Shedfield              38086.36
## 5: E01023270 Winchester 005F                          St Paul              37360.82
## 6: E01023271 Winchester 013B           Whiteley and Shedfield              37185.80
##    beis_GBPtotal_c_perdw
## 1:             11522.747
## 2:             11073.010
## 3:             10858.171
## 4:              9331.159
## 5:              9153.402
## 6:              9110.522
##     LSOA11CD        LSOA01NM                WD18NM CREDStotal_kgco2e_pdw
## 1: E01023285 Winchester 013E Southwick and Wickham              12459.64
## 2: E01023256 Winchester 006C        St Bartholomew              11795.60
## 3: E01023252 Winchester 007A            St Michael              11775.04
## 4: E01023250 Winchester 005C           St Barnabas              11644.20
## 5: E01023221 Winchester 012B      Bishop's Waltham              11309.85
## 6: E01023261 Winchester 008B               St Luke              11275.19
##    beis_GBPtotal_c_perdw
## 1:              3052.612
## 2:              2889.921
## 3:              2884.886
## 4:              2852.829
## 5:              2770.912
## 6:              2762.421

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.16  531.41  601.39  601.12  696.02  927.03
##     LSOA11CD        LSOA01NM                           WD18NM gasTCO2e_pdw
## 1: E01023230 Winchester 010D Badger Farm and Oliver's Battery     3.783790
## 2: E01023241 Winchester 003B          Wonston and Micheldever     3.693281
## 3: E01023226 Winchester 010A        Colden Common and Twyford     3.365778
## 4: E01023268 Winchester 005D                          St Paul     3.363035
## 5: E01023244 Winchester 009D Badger Farm and Oliver's Battery     3.308866
## 6: E01023266 Winchester 008D                          St Paul     3.211447
##    beis_GBPtotal_c_gas_perdw
## 1:                  927.0286
## 2:                  904.8538
## 3:                  824.6157
## 4:                  823.9436
## 5:                  810.6722
## 6:                  786.8045
##     LSOA11CD        LSOA01NM                           WD18NM gasTCO2e_pdw
## 1: E01023252 Winchester 007A                       St Michael    1.6836555
## 2: E01023251 Winchester 006A                       St Michael    1.6509760
## 3: E01023224 Winchester 014A            Southwick and Wickham    1.5291709
## 4: E01023243 Winchester 009C Badger Farm and Oliver's Battery    1.4714619
## 5: E01023280 Winchester 004D      Alresford and Itchen Valley    1.2586180
## 6: E01023225 Winchester 004A                Upper Meon Valley    0.1026763
##    beis_GBPtotal_c_gas_perdw
## 1:                 412.49561
## 2:                 404.48911
## 3:                 374.64688
## 4:                 360.50816
## 5:                 308.36141
## 6:                  25.15568

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   190.7   234.3   265.1   277.4   303.4   464.8
##     LSOA11CD        LSOA01NM                           WD18NM elecTCO2e_pdw
## 1: E01023225 Winchester 004A                Upper Meon Valley      1.896978
## 2: E01023236 Winchester 002A      Alresford and Itchen Valley      1.774252
## 3: E01023229 Winchester 009A Badger Farm and Oliver's Battery      1.744728
## 4: E01023245 Winchester 013A           Whiteley and Shedfield      1.642781
## 5: E01023280 Winchester 004D      Alresford and Itchen Valley      1.641382
## 6: E01023274 Winchester 003E          Wonston and Micheldever      1.561983
##    beis_GBPtotal_c_elec_perdw
## 1:                   464.7597
## 2:                   434.6917
## 3:                   427.4583
## 4:                   402.4814
## 5:                   402.1386
## 6:                   382.6860
##     LSOA11CD        LSOA01NM                           WD18NM elecTCO2e_pdw
## 1: E01023243 Winchester 009C Badger Farm and Oliver's Battery     0.8651011
## 2: E01023251 Winchester 006A                       St Michael     0.8545545
## 3: E01032860 Winchester 013G           Whiteley and Shedfield     0.8455902
## 4: E01023237 Winchester 002B                      The Worthys     0.8438473
## 5: E01023261 Winchester 008B                          St Luke     0.7990677
## 6: E01023260 Winchester 008A                          St Luke     0.7783170
##    beis_GBPtotal_c_elec_perdw
## 1:                   211.9498
## 2:                   209.3658
## 3:                   207.1696
## 4:                   206.7426
## 5:                   195.7716
## 6:                   190.6877

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   489.9   761.4   874.1   878.5   974.4  1280.3

4.2.3.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw
##       0%      25%      50%      75%     100% 
## 11275.19 18731.61 24005.72 31004.27 47031.62
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##           V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw
##  1: 20.51384                  2285.256                  436.6463                    0.0000
##  2: 11.30985                  1379.801                    0.0000                    0.0000
##  3: 26.70112                  2285.256                 1292.1568                  989.2112
##  4: 20.73296                  2285.256                  490.3313                    0.0000
##  5: 20.88792                  2285.256                  528.2966                    0.0000
##  6: 24.63309                  2285.256                 1292.1568                  230.2465
##  7: 23.46564                  2285.256                 1159.8370                    0.0000
##  8: 17.32122                  2113.188                    0.0000                    0.0000
##  9: 24.73746                  2285.256                 1292.1568                  268.5476
## 10: 31.63384                  2285.256                 1292.1568                 2799.5188
##     beis_GBPtotal_sc2_perdw
##  1:                2721.903
##  2:                1379.801
##  3:                4566.624
##  4:                2775.588
##  5:                2813.553
##  6:                3807.660
##  7:                3445.093
##  8:                2113.188
##  9:                3845.961
## 10:                6376.932
Table 4.2: Data summary
Name …[]
Number of rows 64
Number of columns 3
Key NULL
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
V1 0 1 24.93 8.69 11.28 18.73 24.01 31.00 47.03 ▆▇▆▂▁
beis_GBPtotal_sc2_perdw 0 1 4415.52 2638.62 1375.57 2300.11 3599.31 6145.88 12027.92 ▇▃▂▂▁
beis_GBPtotal_sc2 0 1 3134190.38 1511997.02 817314.60 1823951.87 2962663.14 4118309.20 6086127.04 ▇▆▆▃▅
##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:     64      286.0057      200.5882

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:               2345.0893                264.6181
## 2:               1957.1116                238.7676
## 3:               2449.4413                264.6181
## 4:               2348.5987                264.6181
## 5:               1529.1709                186.5589
## 6:                102.6763                 12.5265
##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:               2345.0893                264.6181                43.14158
## 2:               1957.1116                238.7676                 0.00000
## 3:               2449.4413                264.6181                68.70784
## 4:               2348.5987                264.6181                44.00140
## 5:               1529.1709                186.5589                 0.00000
## 6:                102.6763                 12.5265                 0.00000
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                       0              307.7597
## 2:                       0              238.7676
## 3:                       0              333.3260
## 4:                       0              308.6195
## 5:                       0              186.5589
## 6:                       0               12.5265
## [1] 17.84687

## [1] 9.672869

## £m total
##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1:     64                200.5882            17.84687             9.672869 114310
## £m total by regions covered
##        region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East     64                200.5882            17.84687             9.672869 114310

4.2.4 Estimate retofit costs

  • from A-E <- £13,300
  • from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

## To retrofit D-E (£m)
## [1] 317.2953
## Number of dwellings: 23857
## To retrofit F-G (£m)
## [1] 50.83369
## Number of dwellings: 1897
## To retrofit D-G (£m)
## [1] 368.129
## To retrofit D-G (mean per dwelling)
## [1] 14179.92
##    meanPerLSOA_GBPm total_GBPm
## 1:         5.752015    368.129
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5.2 Scenario 2

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.209   1.887   2.442   2.632   3.097   5.120
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.15   14.31   16.12   16.75   18.33   35.11
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Highest retofit sum cost
##      LSOA11CD        LSOA11NM                           WD18NM retrofitSum yearsToPay
##  1: E01023225 Winchester 004A                Upper Meon Valley    13987309   35.11391
##  2: E01023236 Winchester 002A      Alresford and Itchen Valley    10788912   15.18130
##  3: E01023245 Winchester 013A           Whiteley and Shedfield     9834178   14.39001
##  4: E01023287 Winchester 001B          Wonston and Micheldever     9399233   14.32065
##  5: E01023229 Winchester 009A Badger Farm and Oliver's Battery     9126369   13.33184
##  6: E01023284 Winchester 013D            Southwick and Wickham     8734774   18.96992
##  7: E01023240 Winchester 003A                      St Barnabas     8118125   14.82935
##  8: E01023280 Winchester 004D      Alresford and Itchen Valley     7835527   21.76513
##  9: E01023252 Winchester 007A                       St Michael     7522515   22.29159
## 10: E01023264 Winchester 007D                       St Michael     7336981   16.51768
##      epc_D_pc  epc_E_pc    epc_F_pc    epc_G_pc
##  1: 0.2673267 0.3267327 0.194059406 0.047524752
##  2: 0.3761468 0.2775229 0.094036697 0.034403670
##  3: 0.4256293 0.1647597 0.068649886 0.020594966
##  4: 0.4574315 0.1010101 0.008658009 0.001443001
##  5: 0.3725055 0.2439024 0.070953437 0.017738359
##  6: 0.2652608 0.1220866 0.046614872 0.011098779
##  7: 0.5180995 0.1312217 0.015837104 0.000000000
##  8: 0.3843844 0.2762763 0.093093093 0.033033033
##  9: 0.3190045 0.1233032 0.019230769 0.007918552
## 10: 0.3160377 0.2547170 0.047169811 0.011792453

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.2.6.2 Scenario 2

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.179   2.341   4.057   4.472   5.950  10.282
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.15   14.31   16.12   16.75   18.33   35.11
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

Comparing pay-back times for the two scenarios - who does the rising block tariff help?

x = y line shown for clarity

5 R environment

5.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • ggplot2 (Wickham 2009)
  • kableExtra (Zhu 2018)
  • knitr (Xie 2016b)
  • rmarkdown (Allaire et al. 2018)
  • skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.
Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.